Estimating regional species richness using a limited number of survey units
Bibliographic record
Abstract
:The accurate and precise estimation of species richness at large spatial scales using a limited number of survey units is of great significance for ecology and biodiversity conservation. We used the distribution data of native fish and resident breeding bird species compiled for two geographic regions in the U.S.A. to evaluate five established (Jackknife-1 and -2, Chao-2, ICE, and Bootstrap methods) and two new (CY-1 and -2) estimators. Both new estimators are based on relationships between species richness per subsample and the mean Jaccard coefficient across multiple pairs of subsamples, but they differ in the way the relationships are fit. The four regional faunas (two regions × two taxonomic groups) exhibited distinct species-occurrence distributions and a range of spatial heterogeneity. Re-sampling techniques were used to generate subsamples of five sizes (0.61-11.5% of a whole region) for examining the effect of sampling effort. With the total number of species recorded in each region taken as the regional richness, CY-1 and -2 were least biased at low sampling effort and CY-2 and Jackknife-2 were least biased at higher sampling effort. The differences in performance could be partially attributed to whether an estimator relied on the number (e.g., Jackknife-1) or the proportion of singletons (CY-1 and -2) for extrapolation. The estimation of fish species richness was more biased and less precise than that of bird species richness. This difference was closely related to how species-occurrence probability varied among species in a fauna (i.e., species-occurrence probability distribution). The estimators tested, particularly CY-2 and Jackknife-2, are useful in estimating regional total species richness; however, more robust methods are needed, which should take the form of species-occurrence probability distributions into account.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".